Risk management analysis is one of the new requirements under MAP-21 that separates transportation asset management programs from business as usual for the state departments of transportation (DOTs). Based on this requirement, each agency will discuss the concept of risk and how it should be incorporated into its transportation asset management program as well as how it informs maintenance practices, asset replacement or rehabilitation, and emergency management and response planning. This will require an agency to provide a list of risk exposures and document its system-wide risk management strategy. This paper presents the results of a state of the practice survey of how agencies are developing their risk-based asset management plan and discusses recommendations for future research. The results show that state highway agencies are increasingly adapting the way they do business to include explicit considerations of risks. At the moment, this consideration of risk is not linked to data, and as a result most agencies do not have a data driven way of tracking risk and updating their risk exposures. The significance of the results highlights the need for further research on data driven risk management and to synthesize methodologies for integrating risk assessment into an agency’s decision-making process.

This paper investigates the physical and fiscal impacts of Iowa's existing biofuel plants and wind power industries. A four-county cluster in northern Iowa and a two-county cluster in southern Iowa were identified through a local agency survey as having a large number of diverse facilities and were selected for analysis of traffic and physical impact. The large-truck traffic patterns on Iowa's secondary and local roads from 2002 to 2008 were analyzed and associated with the pavement condition and county maintenance expenditures. A trend of increased maintenance costs in the year after a biofuel plant became operational as well as during the construction period was observed. Large-truck traffic also increased dramatically during the construction period and then dropped after the plant became operational, but not to the levels before the plant's construction. The major road damage associated with wind farms occurred during construction activities and predominantly on gravel roads. Face-to-face interviews with county engineers were conducted to validate the observed trends and discuss the limitations of the data. Finally, with an expanded sample of 24 counties, one-way panel data regression models were developed to estimate pavement condition and maintenance costs as a function of vehicle miles traveled, plant capacity and years of operation, corn and soybean production, and soil and environmental conditions.

Safety performance is a crucial component of highway network performance evaluation. Besides their devastating impact on roadway users, traffic crashes lead to substantial economic losses on both personal and societal levels. Due to the complexity of crash events and the unique conditions in each country and state, empirical local calibration for the correlation between attributes of interest and the safety performance is always recommended. Limited studies have established a procedure to analyze the impact of pavement condition on traffic safety in a risk analysis scheme. This study presents a thorough analysis of some roadway departure crashes which occurred in Iowa between 2006 and 2016. All crash records were mapped onto one-mile segments with known traffic volume (i.e., AADT), posted speed limits (SL), skid numbers (SN), ride qualities (IRI), and rut depths (RD) in a geographic information system (GIS) database. The crash records were correlated to the pavement surface condition (i.e., SN, IRI, and RD) using negative binomial regression models. Moreover, a novel risk analysis framework is introduced to perform crash risk assessment and evaluate the possible consequences for a given combination of events. The analysis shows a significant impact of pavement skid resistance on roadway-departure crashes under all accident conditions and severities. Risk analysis will facilitate coordination between the pavement management system and safety management system in the future, which will help with optimizing the overall highway network performance.Several studies have investigated the relationship between pavement surface friction and crash risk, and could quantify significant correlations between them. However, these correlations can be delusive and challenging to derive due to the highly varied and uncontrolled conditions during a crash event [7,8]. One of the most extensive efforts in this research area is the National Corporative Research Program (NCHRP) project 01-43 "Guide for Pavement Friction". The project report was published in 2010 [9]. In the report, quantitative correlations between wet crash rates and pavement friction values could be found in more than 15 studies in the literature. The study focused on describing and illustrating the importance of friction in highway safety, as well as the principles of pavement friction characterization. Furthermore, the study presented valuable information on (a) the management of friction on existing highway pavements, and (b) the design of new highway surfaces with adequate friction. This information focuses on techniques for monitoring friction and crashes, and determining the need for remedial action. However, two components, friction and safety management, are still disconnected and not synchronized efficiently.Pavement friction develops between the tire and the pavement due to the hysteresis and adhesion mechanisms at the tire-pavement interface [9,10]. The two mechanisms, strongly depend on the contact surface area between the tire and the pavement surface, wh...

The collection of pavement condition data is one of the most important and costly elements of operating a pavement management system (PMS). This function is crucial, as business decisions rely on it: a PMS should be able to prioritize maintenance, rehabilitation, and reconstruction effectively. Pavement condition data are usually transformed into a numerical rating system [a pavement condition index (PCI)] that qualitatively describes individual pavement segments or a network. The Iowa Department of Transportation's PCI is calculated by using PCI equations that are based on statistical regression analysis. Different attributes are used for different pavement families. The study summarized here aimed to develop a new condition index that provides a consistent, unified approach to rating pavements in Iowa. The proposed system has a 100-point scale that is based on five indexes derived from specific distress data or pavement properties and an overall index that combines individual indexes with weighting factors. The indexes cover cracking, ride, rutting, faulting, and friction. The cracking index is formed by combining cracking data (i.e., transverse cracking, longitudinal cracking, wheelpath cracking, and alligator cracking indexes). Ride, rutting, and faulting indexes use the international roughness index, rut depth, and fault height, respectively. The proposed overall PCI is made up of 40% cracking index, 40% ride index, and 20% faulting index for portland cement concrete pavements and 40% cracking index, 40% ride index, and 20% rutting index for asphalt concrete pavements. The proposed condition index was compared with the current PCI and, in general, was found to offer fairly good correlation.

A 2017 survey of the state of practice on how agencies are developing their risk-based asset management plan shows that state highway agencies are increasingly adapting the way they do business to include explicit considerations of risks. At the moment, this consideration of risk is not linked to data. Hence, there is a lack of integration of risk management in driving strategic cross-asset programming and decision-making. This paper proposes and implements a risk management database framework as the missing piece in the full implementation of a risk-based transportation asset management program. This risk management database framework utilizes Geographic Information Systems (GIS) and Application Programming Interface (API) to implement a risk management database of all the relevant variables an agency needs for risk modeling to improve risk monitoring, risk register updates, and decision-making. This approach allows the use of existing enterprise as well as legacy data collection systems, which eliminates the need for any capital-intensive implementation cost. Furthermore, it provides transportation agencies with the ability to track risk in quantitative terms, a framework for prioritizing risk, and the development of an actionable plan for risk mitigation. In this paper, the implementation of the fully integrated GIS-enabled risk management database employs the Iowa department of transportation (DOT) data and risk register.